Beams to monitor

ABSTRACT

To configure a set of user devices, which comprises one or more user devices, to monitor a subset of beams, at least one past beam sequence indicating one or more beams, which served the set is determined, and inputted to a trained model which outputs a probability distribution. Then as many beams as is a number of beams determined for the set to monitor is selected from the probability distribution according to a first criteria, and the set of user devices is configured to monitor and report beams in the beam group. Past beam sequences are also used in training. From the past sequences, set-specifically, past beams that served a set within a first time interval are determined to be used as training data, and future beams that served the set within a second time interval following the first time interval are determined to be used as validation data.

TECHNICAL FIELD

Various example embodiments relate to wireless communications and,particularly, to beam monitoring.

BACKGROUND

Wireless communication systems are under constant development. Forexample, beamforming may be used to compensate for high path loss ratesthereby increasing capacity and coverage. Beamforming is a communicationtechnique in which a transmitter transmits a directional transmissionbeam towards a receiver.

BRIEF DESCRIPTION

The scope of protection sought for various embodiments of the inventionis set out by the independent claims. The embodiments, examples andfeatures, if any, described in this specification that do not fall underthe scope of the independent claims are to be interpreted as examplesuseful for understanding various embodiments of the invention.

According to an aspect there is provided an apparatus comprising atleast one processor; and at least one memory including computer programcode, the at least one memory and computer program code configured to,with the at least one processor, cause the apparatus at least toperform: determining, for a set of user devices, at least one past beamsequence indicating one or more beams, which served the set of userdevices, wherein the set of user devices comprises one or more userdevices; inputting the at least one past beam sequence to a trainedmodel which outputs a probability distribution; determining the numberof beams the set of user devices is to monitor; determining a beam groupfor the set of user devices by selecting from the probabilitydistribution according to a first criteria as many beams as is thenumber of beams; configuring the set of user devices to monitor andreport beams in the beam group.

In an embodiment, the at least one memory and computer program codeconfigured to, with the at least one processor, cause the apparatus toinput as the past beam sequence at least beam indexes of the past beams.

In an embodiment, the first criteria is to select the beams according tothe probability order.

In an embodiment, the at least one memory and computer program codeconfigured to, with the at least one processor, cause the apparatus to,when the probability distribution is a matrix, perform the following todetermine the beam group: concatenating the outputs of the matrix to afirst vector; defining a second vector having beam indexes repeated sothat the length of the second vector equals to the first vector; sortingthe first vector in a descending order; shuffling the second vectorcorrespondingly to the sorting, the result being a third vector;eliminating repetitions from the third vector; and selecting from thethird vector, starting from the beginning, to the beam group as manybeam indexes as is the number of beams the set of user devices is tomonitor.

In an embodiment, the at least one memory and computer program codeconfigured to, with the at least one processor, cause the apparatus todetermine the number of beams the set of user devices is to monitor atleast based on user device capabilities and capabilities of theapparatus.

In an embodiment, the at least one memory and computer program codeconfigured to, with the at least one processor, cause the apparatus todetermine a length of the past beam sequence based on user devicemobility characteristics and/or based on channel characteristics and/orbased on beam widths.

In an embodiment, the at least one memory and computer program codeconfigured to, with the at least one processor, cause the apparatus tostore received reports comprising beam measurement results.

In an embodiment, the at least one memory and computer program codeconfigured to, with the at least one processor, cause the apparatus totrain the trained model by performing: determining, from past beammeasurement reports, set-specifically for sets of user devices, pastbeams that served a set of user device within a first time interval, andfuture beams that served the set of user device within a second timeinterval following the first time interval; determining from the futurebeams a non-repetitive set of future beams, wherein the non-repetitiveset comprises a beam only once; and using the past beams as trainingdata and corresponding non-repetitive sets as validation data.

According to an aspect there is provided an apparatus comprising atleast one processor; and at least one memory including computer programcode, the at least one memory and computer program code configured to,with the at least one processor, cause the apparatus at least toperform: determining, from past beam measurement reports,set-specifically for sets of user devices, past beams that served a setof user device within a first time interval, and future beams thatserved the set of user device within a second time interval followingthe first time interval, wherein a set of user devices comprises one ormore user devices; determining from the future beams a non-repetitiveset of future beams, wherein the non-repetitive set comprises a beamonly once; and using the past beams as training data and correspondingnon-repetitive sets as validation data to train a model which outputs aprobability distribution of future beams.

In an embodiment, the model is based on a convolutional neural networkand is configured to output the probability distribution in a form of amatrix.

According to an aspect there is provided a method comprising:determining, for a set of user devices, at least one past beam sequenceindicating one or more beams, which served the set of user devices,wherein the set of user devices comprises one or more user devices;inputting the at least one past beam sequence to a trained model whichoutputs a probability distribution; determining the number of beams theset of user devices is to monitor; determining a beam group for the setof user devices by selecting from the probability distribution accordingto a first criteria as many beams as is the number of beams; configuringthe set of user devices to monitor and report beams in the beam group.

According to an aspect there is provided a method comprising:determining, from past beam measurement reports, set-specifically forsets of user devices, past beams that served a set of user device withina first time interval, and future beams that served the set of userdevice within a second time interval following the first time interval,wherein a set of user devices comprises one or more user devices;determining from the future beams a non-repetitive set of future beams,wherein the non-repetitive set comprises a beam only once; and using thepast beams as training data and corresponding non-repetitive sets asvalidation data to train a model which outputs a probabilitydistribution of future beams.

According to an aspect there is provided a computer program comprisinginstructions for causing an apparatus to perform at least one of a firstprocess and a second process, wherein the first process comprises thefollowing: determining, for a set of user devices, at least one pastbeam sequence indicating one or more beams, which served the set of userdevices, wherein the set of user devices comprises one or more userdevices; inputting the at least one past beam sequence to a trainedmodel which outputs a probability distribution; determining the numberof beams the set of user devices is to monitor; determining a beam groupfor the set of user devices by selecting from the probabilitydistribution according to a first criteria as many beams as is thenumber of beams; configuring the set of user devices to monitor andreport beams in the beam group; and wherein the second process comprisesthe following: determining, from past beam measurement reports,set-specifically for sets of user devices, past beams that served a setof user device within a first time interval, and future beams thatserved the set of user device within a second time interval followingthe first time interval; determining from the future beams anon-repetitive set of future beams, wherein the non-repetitive setcomprises a beam only once; and using the past beams as training dataand corresponding non-repetitive sets as validation data to train amodel which outputs a probability distribution of future beams.

According to an aspect there is provided a computer readable mediumcomprising program instructions for causing an apparatus to perform atleast one of a first process and a second process: wherein the firstprocess comprises the following: determining, for a set of user devices,at least one past beam sequence indicating one or more beams, whichserved the set of user devices, wherein the set of user devicescomprises one or more user devices; inputting the at least one past beamsequence to a trained model which outputs a probability distribution;determining the number of beams the set of user devices is to monitor;determining a beam group for the set of user devices by selecting fromthe probability distribution according to a first criteria as many beamsas is the number of beams; configuring the set of user devices tomonitor and report beams in the beam group; and wherein the secondprocess comprises the following: determining, from past beam measurementreports, set-specifically for sets of user devices, past beams thatserved a set of user device within a first time interval, and futurebeams that served the set of user device within a second time intervalfollowing the first time interval; determining from the future beams anon-repetitive set of future beams, wherein the non-repetitive setcomprises a beam only once; and using the past beams as training dataand corresponding non-repetitive sets as validation data to train amodel which outputs a probability distribution of future beams.

In an embodiment, the computer readable medium is a non-transitorycomputer readable medium.

BRIEF DESCRIPTION OF DRAWINGS

Embodiments are described below, by way of example only, with referenceto the accompanying drawings, in which

FIG. 1 illustrates an exemplified wireless communication system;

FIG. 2 illustrate an example functionality;

FIG. 3 illustrates an example of information exchange andfunctionalities;

FIG. 4 illustrates an example functionality;

FIG. 5 illustrates examples of different data sets;

FIG. 6 illustrates an example functionality;

FIG. 7 illustrates a schematic diagram of a trainable model; and

FIGS. 8 and 9 are schematic block diagrams.

DETAILED DESCRIPTION OF SOME EMBODIMENTS

The following embodiments are examples. Although the specification mayrefer to “an”, “one”, or “some” embodiment(s) in several locations, thisdoes not necessarily mean that each such reference is to the sameembodiment(s), or that the feature only applies to a single embodiment.Single features of different embodiments may also be combined to provideother embodiments. Furthermore, words “comprising” and “including”should be understood as not limiting the described embodiments toconsist of only those features that have been mentioned and suchembodiments may contain also features/structures that have not beenspecifically mentioned. Further, although terms including ordinalnumbers, such as “first”, “second”, etc., may be used for describingvarious elements, the structural elements are not restricted by theterms. The terms are used merely for the purpose of distinguishing anelement from other elements. For example, a first element could betermed a second element, and similarly, a second element could be alsotermed a first element without departing from the scope of the presentdisclosure.

Embodiments and examples described herein may be implemented in anycommunications system comprising wireless connection(s). In thefollowing, different exemplifying embodiments will be described using,as an example of an access architecture to which the embodiments may beapplied, a radio access architecture based on new radio (NR, 5G) or longterm evolution advanced (LTE Advanced, LTE-A), without restricting theembodiments to such an architecture, however. It is obvious for a personskilled in the art that the embodiments may also be applied to otherkinds of communications networks having suitable means by adjustingparameters and procedures appropriately. Some examples of other optionsfor suitable systems are the universal mobile telecommunications system(UMTS) radio access network (UTRAN or E-UTRAN), long term evolution(LTE, the same as E-UTRA), beyond 5G, wireless local area network (WLANor WiFi), worldwide interoperability for microwave access (WiMAX),Bluetooth®, personal communications services (PCS), ZigBee®, widebandcode division multiple access (WCDMA), systems using ultra-wideband(UWB) technology, sensor networks, mobile ad-hoc networks (MANETs) andInternet Protocol multimedia subsystems (IMS) or any combinationthereof.

FIG. 1 depicts examples of simplified system architectures only showingsome elements and functional entities, all being logical units, whoseimplementation may differ from what is shown. The connections shown inFIG. 1 are logical connections; the actual physical connections may bedifferent. It is apparent to a person skilled in the art that the systemtypically comprises also other functions and structures than those shownin FIG. 1 .

The embodiments are not, however, restricted to the system given as anexample but a person skilled in the art may apply the solution to othercommunication systems provided with necessary properties.

The example of FIG. 1 shows a part of an exemplifying radio accessnetwork.

FIG. 1 shows user devices 101 and 101′ configured to be in a wirelessconnection on one or more communication channels in a cell with anaccess node (such as (e/g)NodeB) 102 providing the cell. The physicallink from a user device to a (e/g)NodeB is called uplink or reverse linkand the physical link from the (e/g)NodeB to the user device is calleddownlink or forward link. It should be appreciated that (e/g)NodeBs ortheir functionalities may be implemented by using any node, host, serveror access point (AP) etc. entity suitable for such a usage.

A communications system 100 typically comprises more than one (e/g)NodeBin which case the (e/g)NodeBs may also be configured to communicate withone another over links, wired or wireless, designed for the purpose.These links may be used for signalling purposes. The (e/g)NodeB is acomputing device configured to control the radio resources ofcommunication system it is coupled to. The NodeB may also be referred toas a base station, an access point or any other type of interfacingdevice including a relay station capable of operating in a wirelessenvironment. The (e/g)NodeB includes or is coupled to transceivers. Fromthe transceivers of the (e/g)NodeB, a connection is provided to anantenna unit that establishes bi-directional radio links to userdevices. The antenna unit may comprise a plurality of antennas orantenna elements. The (e/g)NodeB is further connected to core network105 (CN or next generation core NGC). Depending on the system, thecounterpart on the CN side can be a serving gateway (S-GW, routing andforwarding user data packets), packet data network gateway (P-GW), forproviding connectivity of user devices (UEs) to external packet datanetworks, or mobile management entity (MME), etc.

The user device (also called UE, user equipment, user terminal, terminaldevice, etc.) illustrates one type of an apparatus to which resources onthe air interface are allocated and assigned, and thus any featuredescribed herein with a user device may be implemented with acorresponding apparatus, such as a relay node. An example of such arelay node is a layer 3 relay (self-backhauling relay) towards the basestation.

The user device typically refers to a portable computing device thatincludes wireless mobile communication devices operating with or withouta subscriber identification module (SIM), including, but not limited to,the following types of wireless devices: a mobile station (mobilephone), smartphone, personal digital assistant (PDA), handset, deviceusing a wireless modem (alarm or measurement device, etc.), laptopand/or touch screen computer, tablet, game console, notebook, andmultimedia device. It should be appreciated that a user device may alsobe a nearly exclusive uplink only device, of which an example is acamera or video camera loading images or video clips to a network. Auser device may also be a device having capability to operate inInternet of Things (IoT) network which is a scenario in which objectsare provided with the ability to transfer data over a network withoutrequiring human-to-human or human-to-computer interaction. The userdevice may also utilise cloud. In some applications, a user device maycomprise a small portable device with radio parts (such as a watch,earphones or eyeglasses) and the computation is carried out in thecloud. The user device (or in some embodiments a relay node, such as amobile termination (MT) part of the integrated access and backhaul (IAB)Node), is configured to perform one or more of user equipmentfunctionalities. The user device may also be called a subscriber unit,mobile station, remote terminal, access terminal, user terminal or userequipment (UE) just to mention but a few names or apparatuses.

Various techniques described herein may also be applied to acyber-physical system (CPS) (a system of collaborating computationalelements controlling physical entities). CPS may enable theimplementation and exploitation of massive amounts of interconnected ICTdevices (sensors, actuators, processors microcontrollers, etc.) embeddedin physical objects at different locations. Mobile cyber physicalsystems, in which the physical system in question has inherent mobility,are a subcategory of cyber-physical systems. Examples of mobile physicalsystems include mobile robotics and electronics transported by humans oranimals.

Additionally, although the apparatuses have been depicted as singleentities, different units, processors and/or memory units (not all shownin FIG. 1 ) may be implemented.

5G enables using multiple input-multiple output (MIMO) antennas, manymore base stations or nodes or corresponding network devices than theLTE (a so-called small cell concept), including macro sites operating inco-operation with smaller stations and employing a variety of radiotechnologies depending on service needs, use cases and/or spectrumavailable. 5G mobile communications supports a wide range of use casesand related applications including video streaming, augmented reality,different ways of data sharing and various forms of machine typeapplications (such as (massive) machine-type communications (mMTC),including vehicular safety, different sensors and real-time control. 5Gis expected to have multiple radio interfaces, namely below 6 GHz,cmWave and mmWave, and also being integradable with existing legacyradio access technologies, such as the LTE. Integration with the LTE maybe implemented, at least in the early phase, as a system, where macrocoverage is provided by the LTE and 5G radio interface access comes fromsmall cells by aggregation to the LTE. In other words, 5G is planned tosupport both inter-RAT operability (such as LTE-5G) and inter-RIoperability (inter-radio interface operability, such as below 6GHz-cmWave, below 6 GHz-cmWave-mmWave). One of the concepts consideredto be used in 5G networks is network slicing in which multipleindependent and dedicated virtual sub-networks (network instances) maybe created within the same infrastructure to run services that havedifferent requirements on latency, reliability, throughput and mobility.

The current architecture in LTE networks is fully distributed in theradio and fully centralized in the core network. The low latencyapplications and services in 5G require to bring the content close tothe radio which leads to local break out and multi-access edge computing(MEC). 5G enables analytics and knowledge generation to occur at thesource of the data. This approach requires leveraging resources that maynot be continuously connected to a network such as laptops, smartphones,tablets and sensors. MEC provides a distributed computing environmentfor application and service hosting. It also has the ability to storeand process content in close proximity to cellular subscribers forfaster response time. Edge computing covers a wide range of technologiessuch as wireless sensor networks, mobile data acquisition, mobilesignature analysis, cooperative distributed peer-to-peer ad hocnetworking and processing also classifiable as local cloud/fog computingand grid/mesh computing, dew computing, mobile edge computing, cloudlet,distributed data storage and retrieval, autonomic self-healing networks,remote cloud services, augmented and virtual reality, data caching,Internet of Things (massive connectivity and/or latency critical),critical communications (autonomous vehicles, traffic safety, real-timeanalytics, time-critical control, healthcare applications).

The communication system is also able to communicate with othernetworks, such as a public switched telephone network or the Internet106, or a server and/or data storage 106 thereof, or utilise servicesprovided by them. The communication network may also be able to supportthe usage of cloud services, for example at least part of core networkoperations may be carried out as a cloud service (this is depicted inFIG. 1 by “cloud” 107). The communication system may also comprise acentral control entity, or a like, providing facilities for networks ofdifferent operators to cooperate for example in spectrum sharing.

Edge cloud may be brought into radio access network (RAN) by utilizingnetwork function virtualization (NVF) and software defined networking(SDN). Using edge cloud may mean access node operations to be carriedout, at least partly, in a server, host or node operationally coupled toa remote radio head or base station comprising radio parts. It is alsopossible that node operations will be distributed among a plurality ofservers, nodes or hosts. Application of cloudRAN architecture enablesRAN real time functions being carried out at the RAN side (in adistributed unit, DU 102) and non-real time functions being carried outin a centralized manner (in a centralized unit, CU 104).

It should also be understood that the distribution of labour betweencore network operations and base station operations may differ from thatof the LTE or even be non-existent. Some other technology advancementsprobably to be used are Big Data and all-IP, which may change the waynetworks are being constructed and managed. 5G (or new radio, NR)networks are being designed to support multiple hierarchies, where MECservers can be placed between the core and the base station or nodeB(gNB). It should be appreciated that MEC can be applied in 4G networksas well.

5G may also utilize satellite communication to enhance or complement thecoverage of 5G service, for example by providing backhauling. Possibleuse cases are providing service continuity for machine-to-machine (M2M)or Internet of Things (IoT) devices or for passengers on board ofvehicles, or ensuring service availability for critical communications,and future railway/maritime/aeronautical communications. Satellitecommunication may utilise geostationary earth orbit (GEO) satellitesystems, but also low earth orbit (LEO) satellite systems, in particularmega-constellations (systems in which hundreds of (nano)satellites aredeployed). Each satellite 103 in the mega-constellation may coverseveral satellite-enabled network entities that create on-ground cells.The on-ground cells may be created through an on-ground relay node 102or by a gNB located on-ground or in a satellite.

It is obvious for a person skilled in the art that the depicted systemis only an example of a part of a radio access system and in practice,the system may comprise a plurality of (e/g)NodeBs, the user device mayhave an access to a plurality of radio cells and the system may comprisealso other apparatuses, such as relay nodes, for example distributedunit (DU) parts of one or more IAB nodes, or other network elements,etc. At least one of the (e/g)NodeBs or may be a Home(e/g)nodeB.Additionally, in a geographical area of a radio communication system aplurality of different kinds of radio cells as well as a plurality ofradio cells may be provided. Radio cells may be macro cells (or umbrellacells) which are large cells, usually having a diameter of up to tens ofkilometers, or smaller cells such as micro-, femto- or picocells. The(e/g)NodeBs of FIG. 1 may provide any kind of these cells. A cellularradio system may be implemented as a multilayer network includingseveral kinds of cells. Typically, in multilayer networks, one accessnode provides one kind of a cell or cells, and thus a plurality of(e/g)NodeBs are required to provide such a network structure.

For fulfilling the need for improving the deployment and performance ofcommunication systems, the concept of “plug-and-play” (e/g)NodeBs hasbeen introduced. Typically, a network which is able to use“plug-and-play” (e/g)Node Bs, includes, in addition to Home (e/g)NodeBs(H(e/g)nodeBs), a home node B gateway, or HNB-GW (not shown in FIG. 1 ).A HNB Gateway (HNB-GW), which is typically installed within anoperator's network may aggregate traffic from a large number of HNBsback to a core network.

In 5G (or new radio, NR) networks cells may be provided by access nodes(base stations), which comprise multiple antenna elements (transmissionreception units), and thereby can form multiple beams. Devices served insuch cells may provide additional beamforming at their end.

Below term “gNB” is used for access nodes and term “user device” is usedfor devices/apparatus served by gNB for the sake of clarity, withoutrestricting the examples to gNB and user device. It should beappreciated that the term user device also includes a user device group,the user devices belonging to the user device group being treated as oneuser device by gNB or are locating in close proximity of each other,thereby sharing similar channel characteristics since and being servedby similar beams. In other words, the term user device means a set ofuser devices, which set may comprise one or more user devices.

For beam management, a user device may monitor and report to gNB one ormore beam-specific reference signals. However, for example, for powerconsumption reasons and/or because of resource limitations, the userdevice may be configured by gNB to monitor and report only a subset ofbeams, or more precisely monitor and report reference signals of thesubset of beams, provided by gNB.

FIG. 2 illustrates an example functionality of gNB when it configuresthe user device.

Referring to FIG. 2 , the configuration procedure is started orrestarted in block 200. The starting/restarting may be triggered whenthe user device changes a serving beam in the same cell or during ahandover to another cell or status (mode) of the user device changes toactive, for example. Then a past beam sequence is determined in block201. The past beam sequence is determined using history information onone or more beams that served the user device and it indicates beamsthat have served the user device. The history information used may alsocomprise history information on one or more other user devices that havebeen served by at least one of the one or more beams or by one or moresimilar beams. For example, the past beam sequence may indicate the oneor more beams that served the user device within the last M framessampled every frame. The criteria described below with block 401relating to extracting/creating past beam sequences can be used hereinas well. The length of the sequence and its scale may be freelydetermined. The length of the sequence may depend on user devicemobility characteristics, for example the speed the user device ismoving, and/or on channel characteristics, beam widths used by gNB. Forexample, sequence 501 in FIG. 5 depicts an example of a past beamsequence comprising beam indexes. In another example, the past beamsequence may comprise 10 beam indexes indicating beams that served theuser device with a scale of 10 ms. The past sequence may start with thelatest serving beam or end with the latest serving beam.

Once the past beam sequence is determined, it is inputted to a trainedmodel in block 202. The trained model may have been created as describedbelow with FIGS. 4 to 7 . The trained model outputs a probabilitydistribution in block 203, which is a matrix of size C×N, wherein C isthe number of outputs of the trained model, and N is the number of beamsgNB has in total. Each element in the matrix shows the probability ofthat beam in the set of beams.

Also K, i.e. the number of beams the user device is to monitor, isdetermined in block 204. The value of K may depend on the number ofbeams the user device is configured to support and/or the number ofbeams gNB is configured to support simultaneously in case all N beamscannot be, or are not, used by gNB at the same time and/or it may be avalue given by an operator, and/or a value stored to the user device,the stored value possibly being smaller than the number of beams theuser device is configured to support. In other words, there may beconstraints which need to be met, or at least taken into account, whenthe number of beams, K, is determined. The user device dependentconstraints are signaled to the network during attach procedure, forexample. It should be appreciated that usually the number to monitor, K,is much more smaller than the total number of beams provided in thecell, N, and that the number to monitor may be bigger or smaller thanthe number of outputs of the trained model, C, or equal to the number ofoutputs of the trained model.

Then a beam group comprising K beams (K unique beam indexes) isdetermined in block 205 by selecting the K most likely beams from theoutputted probability distribution. In other words, the matrix ispostprocessed in block 205.

The matrix may be postprocessed for example as follows:

-   -   1. Concatenate the C outputs (predictions), for example if C is        4, the concatenated outputs may be expressed as follows:        P=[pred1 pred2 pred3 pred4]    -   2. Define the beam index vector u=[1, 2 . . . N]    -   3. Define U=[u u u u], wherein U is a vector concatenating the        beam index vector C times    -   4. Sort the concatenated C outputs in P in descending order, for        example as P:=P(I), wherein I is a permutation vector.    -   5. Shuffle U using the permutation vector I used to sort P in        descending order: U:=U(I)    -   6. Eliminate the repetitions in U after shuffling: U:=unique(U)    -   7. Chose most likely K beams (K<=N) which correspond first K        elements of U. For example, first K elements in U may be chosen.

Once the beam group comprising K unique beams is determined in block205, the user device is configured in block 206 to monitor and reportthe beams in the beam group. For example, transmission informationconfiguration table, called TCI table, is updated (reconfigured) to haveper beam a valid TCI state for reference signals to be monitored andreported, and then sending the updated TCI table to the user device iscaused.

In another embodiment, it is checked whether the TCI table before theprocedure is started is the same as the TCI table after the procedure,and if they are the same, block 206 may be omitted.

In an example, in which a sequence ends with the latest serving beam,the result of block 201 can be 1 1 2 2 3 3 3 4 4 4, and if K=4, K mostlikely beams could be 4, 5, 7 and 10 (result of block 205).

The above functionality of FIG. 2 may be implemented to determinecluster based beam groups, i.e. beams serving a cluster of user devices,by performing blocks 201 to 203 per user device, after which the outputmatrices are aggregated, K is determined based on gNB capabilitiesand/or user device capabilities and/or constraints, as explained above,and in block 205 from the aggregated output matrices a beam groupcomprising K beams is determined.

FIG. 3 illustrates an example of information exchange andfunctionalities between gNB and served user devices, depicted by UE-1,UE-2, UE-3, UE-N.

Referring to FIG. 3 , gNB determines in block 3-1 userdevice-specifically configuration for reference signal reporting, andcauses sending the configurations in messages 3-2 a, 3-2 b, 3-2 c, 3-2d. Messages 3-2 a, 3-2 b, 3-2 c, 3-2 d may be layer 3 signallingmessages (RRL (radio resource control layer) messages). For example,message 3-2 a may request a measurement report for beams 1 and N,message 3-2 b periodic measurement reports for beams 1, 32 and 36,message 3-2 c periodic measurement reports for a serving beam, andmessage 3-2 d measurement report for the two strongest beam.

When the configuration information is received (messages 3-2 a, 3-2 b,3-2 c, 3-2 d), the user device (UE-1, UE-2, UE-3, UE-N) configuresitself correspondingly (block 3-3 a, 3-3 b, 3-3 c, 3-3 d), monitors andmeasures the beams, and sends reports (messages 3-4 a, 3-4 b, 3-4 c, 3-4d) to gNB according to its configuration, until a new configuration isreceived from gNB. The reports may be sent using layer 2/layer 1signalling.

When gNB receives a report (message 3-4 a, 3-4 b, 3-4 c, 3-4 d) gNBstores (block 3-5) the report to its memory. Naturally it also processesthe report to determine whether the user device should change a beam,for example. For example, gNB may store information illustrated in afollowing table. It should be appreciated that the following table onlyshows only a small piece of information stored for one user device toillustrate what may be stored, without limiting implementations to thefollowing. In the table, RSRP means a reference signal received powerand L1 layer 1.

Beam 1 L1- Beam 2 L1- Beam N L1- RSRP RSRP RSRP UE# time stamp [dBm][dBm] . . . [dBm] 1 10 ms −68 −64 −107 1 20 ms −44 −60 −80 1 52 ms −110−85 1 55 ms −50 −48

Naturally, each time gNB determines a new configuration (block 3-1) fora user device, the configuration is sent to the user device, which thenconfigures itself correspondingly.

The information stored in block 3-5 may be used for creating trainingsets used for obtaining the trained model.

FIG. 4 illustrates an example functionality of an apparatus configuredto create training sets using measurement reports from one user device,UE-k, with the help of FIG. 5 . The apparatus performing thefunctionality may be gNB or a cloud server (apparatus), for example.

Referring to FIG. 4 , a beam sequence of length L for a user device(UE-k) is retrieved in block 401 from a memory whereto gNB storedmeasurement report data. In other words, from the memory entries for theuser device are extracted so that a beam sequence of L is created unlessit already exists in the memory. The beam sequence may beextracted/created using, for example, following criteria: beam indexevery 10 ms (sampling interval, for example length of a slot), if adwell time (time spent on a beam) at the best beam at the sampling timet is less than a predetermined time, for example 5 ms, maintain theprevious team (beam at sampling time t-1) in the sequence, and ifswitching power threshold is less than or equal to a predeterminedvalue, for example 3 dB, maintain the previous beam.

Then from the beam sequence 501 one or more samples are determined inblock 402 using training data generation criteria. The training datageneration criteria may define the number of samples, P, that are to beincluded into a past sequence of the sample and the number of samples,F, that are to be included into a future sequence of the sample, and astride between different samples. In the example illustrated in FIG. 5 ,the stride is 2 slots. The first sample comprises a past sequence 502-1and a future sequence 503-1. The second sample comprises a past sequence502-2 and a future sequence 503-2. The third sample comprises a pastsequence 502-3 and a future sequence 503-3. Corresponding sample setscan be created until the future sequence comprises the last F samples inthe beam sequence 1.

Then from the future sequences F non-repetitive future samples F′ aredetermined in block 403. A non-repetitive future sample F′ comprises abeam only once. If beam indexes are used as beam identifiers, a futuresequence F may comprise the same beam index once, twice, or more, but anon-repetitive future sample F′ comprise a beam index only once.

Then, for each future sample, it is compared in block 404 whether thenumber of beams, F′, in the non-repetitive future sample is less thanthe number of outputs, C, in the model to be trained. If the number ofbeams, F′, in the non-repetitive future sample is not less than thenumber of outputs, C, in the model to be trained (block 404: no), then afuture set of C beams are determined from the future sequence in block405. Once determined, the future set and corresponding past sequence areaccumulated in block 406 to a training set. If the number of beams, F′,in the non-repetitive future sample is less than the number of outputs,C, in the model to be trained (block 404: yes), a future set of beams isdetermined in block 407 to have the beams in the non-repetitive futuresample F′ and then zeroes (0) so that the future set comprises C fields.Once determined, the future set and corresponding past sequence areaccumulated in block 406 to a training set. Referring to FIG. 5 , fromthe future sequence 503-1 a future set 504-1 is determined in blocks 403and 407, and sequence 502-1 and the future set 504-1 are accumulated tothe training set. Correspondingly, from the future sequence 503-2 afuture set 504-2 is determined in blocks 403 and 407, and sequence 502-2and the future set 504-2 are accumulated to the training set, and fromthe future sequence 503-3 a future set 504-3 is determined in blocks 403and 407, and sequence 502-3 and the future set 504-3 are accumulated tothe training set.

In another implementation the past sequence and the future sequence areaccumulated to the training set, and the future set is determined duringtraining, to be compared with the output.

FIG. 6 illustrates an example functionality of an apparatus configuredto train an artificial intelligent based model to obtain the trainedmodel usable by gNB to obtain probability distribution, to be used fordetermining configuration information defining which beams amongstmultiple beams to monitor, as explained with FIG. 2 , for example. Theapparatus performing the functionality may be gNB or a cloud server(apparatus), for example. The example functionality describes basicprinciples used for training and retraining, when need arises, themodel. In the example it is assumed that there are enough measurementreport data to create training data and validation data. The model to betrained is based on machine learning. A wide range of such algorithmsare available and any of them, or any corresponding future algorithmsmay be used as a basis for the model to be trained. The machine learningmay be classification, using an artificial neural network, or a supportvector machine, or regression, to name couple of examples. Naturally,any other machine learning, such as image learning, rule-based learningand explanation-based learning, may be used. In FIG. 7 one example of amodel is disclosed in detail.

The training may be triggered periodically, and/or in response todetecting a performance degradation and/or in response to configurationchanges in gNB.

Referring to FIG. 6 , it is assumed that data comprising past sequencesand future sets is available, the past sequences being used as trainingdata and the future sets being used as the validation data (groundtruth, the correct future set). The training is triggered by inputtingin block 601 the past sequences to the model. An initial error, whichmay be set arbitrarily, may also inputted in block 601. The thusobtained model outputs are compared in block 602 with correspondingfuture sets. Depending on an implementation of the training algorithmmay select from the outputs one or more most likely next beams andcompare whether they are the some as corresponding correct one or morebeams in the future sets. In other words, a categorical loss function isused. If the comparison indicates that end criteria is not met (block603: no), the past sequences and error information obtained duringcomparison is inputted to the model in block 604 to tweak the parametersof the neural network and the process returns to block 602 to compareoutput with the future sets. To minimize the error, the errorinformation inputted is determined by using a categorical loss function.An end criteria may be that the output is the same as the correspondingfuture set.

When the model is determined to be accurate, i.e. the end criteria ismet (block 603: yes), the trained model is stored in block 605 for useby gNB.

In other words, the machine learning model is trained using the trainingdata in an iterative manner until the model fulfils end criteria(accuracy criteria). The accuracy criteria may be that outputtedestimated predictions of conditions should correspond to the predictionsof conditions obtained from the remote monitoring analysis. The trainingmay be supervised learning or semi-supervised learning. When the machinelearning model is using an artificial neural network, during theiterations weights of nodes in the artificial neural network based modelmay be adjusted.

FIG. 7 is a block diagram illustrating in a high level an examplestructure of a trainable artificial intelligence based model. Theillustrated model 700 is based on convolutional neural network andcomprises an input layer 701, three convolutional layers 702 703, 704,for example convolutional layers convict for processing sequences, aflatten layer 705, a dense layer 706 and an output layer 707 comprisingin the illustrated example four outputs 707-1, 707-2, 707-3, 707-4. Itshould be appreciated that the output layer may comprise any number ofoutputs.

The input layer 701 does not comprise any parameters, but receives thepast sequences as input 71. When the model is trained, the input layer701 receives during the training iterations also error information input72. The error information 72 is kind of a loss between the true set(target set) and predicted set (outputted set). Implicitly, during thetraining the error information input 72 is used to tweak the parametersof the neural network. When the trained model is in use, no errorinformation 72 is inputted.

The input layer forwards its input to the first convolutional layer 702,which in the example comprises 6344 trainable parameters. The output ofthe first convolutional layer 702 is inputted to the secondconvolutional layer 703, which in the example comprises 132 trainableparameters. The output of the second convolutional layer 703 is inputtedto the third convolutional layer 704, which in the example comprises 18trainable parameters. The output of the third convolutional layer 704 isinputted to the flatten layer 705, which in the example does notcomprise any parameters. The output of the flatten layer 705 is inputtedto the dense layer 706, which in the example comprises 8950 trainableparameters. The output of the dense layer is inputted to the outputs707-1, 707-2, 707-3, 707-4, an output in the output layer comprising5049 trainable parameters. Then, using the categorical loss functioninput 72 is created by comparing results obtained from the outputs tovalidation data (future sets).

Altogether in the illustrated example the model comprises 35 640trainable parameters and zero non-trainable parameters.

The use of probability distribution, i.e. the simplification, enablesuse of trained models based on convolutional neural networkarchitectures, which are less complicated and hence easier to implementthan trained models based on long short-term memory (LSTM) recurrentneural network architecture. However, in another embodiment the trainedmodel may be based LSTM encoder configured to determine, using the aboveinput (past sequences of beams) and principles, as the probabilitydistribution, beams in future slots, and aggregate beam groups based thebeams in future slots.

Although in the above examples the input to the trained model is thepast sequences of beams, indicated by beam index values, additionalinformation may be used as part of the past sequence input, also in thetraining. The additional information may comprise reference signalreceived power (RSRP) information and/or channel quality information,for example channel quality indicators (CQIs), and/or location(s) of theuser device.

It should be appreciated that the above examples can be used fordetermining, based on the history information, uplink beams or downlinkbeams or both uplink and downlink beams. Further, history information ofdownlink beams can be used for predicting K most likely uplink beams,and vice versa. Naturally, history information of downlink beams can beused for predicting K most likely downlink beams, history information ofuplink beams can be used for predicting K most likely uplink beams andhistory information of both uplink and downlink beams can be used topredict K most likely uplink beams and/or K most likely downlink beams,wherein the value of K for uplink may be the same or different than thevalue of K for downlink. Further, uplink beam(s) serving a user devicemay be the same, or contain one or more different beams than downlinkbeam(s) serving the user device.

The blocks, related functions, and information exchanges described aboveby means of FIGS. 2 to 7 are in no absolute chronological order, andsome of them may be performed simultaneously or in an order differingfrom the given one. Other functions can also be executed between them orwithin them, and other information may be transmitted, and/or otherrules applied or selected. Some of the blocks or part of the blocks orone or more pieces of information can also be left out or replaced by acorresponding block or part of the block or one or more pieces ofinformation.

FIGS. 8 and 9 illustrate apparatuses comprising a communicationcontroller 810, 910 such as at least one processor or processingcircuitry, and at least one memory 820, 920 including a computer programcode (software, algorithm) ALG. 821, 921, wherein the at least onememory and the computer program code (software, algorithm) areconfigured, with the at least one processor, to cause the respectiveapparatus to carry out any one of the embodiments, examples andimplementations described above. FIG. 8 illustrates an apparatusconfigured to configure user devices to monitor and report beams, andFIG. 9 illustrates an apparatus for training a model used by theapparatus in FIG. 8 . The apparatuses of FIGS. 8 and 9 may be electronicdevices. Further, an apparatus may be a combination of the apparatusillustrated in FIG. 8 and the apparatus illustrated in FIG. 9 .

Referring to FIGS. 8 and 9 , the memory 820, 920 may be implementedusing any suitable data storage technology, such as semiconductor basedmemory devices, flash memory, magnetic memory devices and systems,optical memory devices and systems, fixed memory and removable memory.The memory may comprise a configuration storage CONF. 822,922, such as aconfiguration database, for at least storing one or more configurations,including beam monitoring configurations at least temporarily. Thememory 820 may further store, measurement reports on beams. The memory820, 920 may further store a data buffer for data waiting to beprocessed (including transmission).

Referring to FIG. 8 , the apparatus, for example gNB, comprises acommunication interface 830 comprising hardware and/or software forrealizing communication connectivity according to one or more wirelessand/or wired communication protocols. The communication interface 830may provide the apparatus with radio communication capabilities withuser devices (terminal devices) camping in one or more cells controlledby the apparatus, as well as communication capabilities towards a wirednetwork.

Digital signal processing regarding transmission and reception ofsignals may be performed in a communication controller 810. Thecommunication interface may comprise standard well-known components suchas an amplifier, filter, frequency-converter, (de)modulator, andencoder/decoder circuitries and one or more antennas.

The communication controller 810 comprises a beam monitoringconfiguration (B-M-C) processing circuitry 811 configured to configureuser devices to monitor and report beams according to any one of theembodiments/examples/implementations described above. The communicationcontroller 810 may control the beam monitoring configuration processingcircuitry 811. Digital signal processing regarding transmission andreception of signals, for example configuration messages and reports onbeams, may be performed in a communication controller 810.

In an embodiment, at least some of the functionalities of the apparatusof FIG. 8 may be shared between two physically separate devices, formingone operational entity. Therefore, the apparatus may be seen to depictthe operational entity comprising one or more physically separatedevices for executing at least some of the processes described withrespect to the sink node.

Referring to FIG. 9 , the apparatus 900 may further comprise acommunication interface 930 comprising hardware and/or software forrealizing communication connectivity according to one or morecommunication protocols. The communication interface 930 may provide theapparatus 900 with communication capabilities with the apparatus of FIG.8 . The communication interface may comprise standard well-known analogcomponents such as an amplifier, filter, frequency-converter andcircuitries, and conversion circuitries transforming signals betweenanalog and digital domains. Digital signal processing regardingtransmission and reception of signals may be performed in acommunication controller 910.

The communication controller 910 comprises a trainer circuitry 911configured to train a model for beam prediction according to any one ofthe embodiments/examples/implementations described above. The trainedcircuitry 911 may communicate the trained model and/or updatedparameters to the apparatus 800 through the communication interface 930.

As used in this application, the term ‘circuitry’ refers to all of thefollowing: (a) hardware-only circuit implementations, such asimplementations in only analog and/or digital circuitry, and (b)combinations of circuits and soft-ware (and/or firmware), such as (asapplicable): (i) a combination of processor(s) or (ii) portions ofprocessor(s)/software including digital signal processor(s), software,and memory(ies) that work together to cause an apparatus to performvarious functions, and (c) circuits, such as a microprocessor(s) or aportion of a microprocessor(s), that require software or firmware foroperation, even if the software or firmware is not physically present.This definition of ‘circuitry’ applies to all uses of this term in thisapplication. As a further example, as used in this application, the term‘circuitry’ would also cover an implementation of merely a processor (ormultiple processors) or a portion of a processor and its (or their)accompanying software and/or firmware. The term ‘circuitry’ would alsocover, for example and if applicable to the particular element, abaseband integrated circuit or applications processor integrated circuitfor a mobile phone or a similar integrated circuit in a server, acellular network device, or another network device.

In an embodiment, at least some of the processes described in connectionwith FIGS. 2 to 7 may be carried out by an apparatus comprisingcorresponding means for carrying out at least some of the describedprocesses. The apparatus may comprise separate means for separate phasesof a process, or means may perform several phases or the whole process.Some example means for carrying out the processes may include at leastone of the following: detector, processor (including dual-core andmultiple-core processors), digital signal processor, controller,receiver, transmitter, encoder, decoder, memory, RAM, ROM, software,firmware, display, user interface, display circuitry, user interfacecircuitry, user interface software, display software, circuit, antenna,antenna circuitry, and circuitry. In an embodiment, the at least oneprocessor, the memory, and the computer program code form processingmeans or comprises one or more computer program code portions forcarrying out one or more operations according to any one of theembodiments/examples/implementations described herein.

According to yet another embodiment, the apparatus carrying out theembodiments/examples comprises a circuitry including at least oneprocessor and at least one memory including computer program code. Whenactivated, the circuitry causes the apparatus to perform at least someof the functionalities according to any one of theembodiments/examples/implementations of FIGS. 2 to 7 , or operationsthereof.

The techniques and methods described herein may be implemented byvarious means. For example, these techniques may be implemented inhardware (one or more devices), firmware (one or more devices), software(one or more modules), or combinations thereof. For a hardwareimplementation, the apparatus(es) of embodiments may be implementedwithin one or more application-specific integrated circuits (ASICs),digital signal processors (DSPs), digital signal processing devices(DSPDs), programmable logic devices (PLDs), field programmable gatearrays (FPGAs), processors, controllers, micro-controllers,microprocessors, other electronic units designed to perform thefunctions described herein, or a combination thereof. For firmware orsoftware, the implementation can be carried out through modules of atleast one chip set (e.g. procedures, functions, and so on) that performthe functions described herein. The software codes may be stored in amemory unit and executed by processors. The memory unit may beimplemented within the processor or externally to the processor. In thelatter case, it can be communicatively coupled to the processor viavarious means, as is known in the art. Additionally, the components ofthe apparatuses (nodes) described herein may be rearranged and/orcomplemented by additional components in order to facilitate theachievements of the various aspects, etc., described with regardthereto, and they are not limited to the precise configurations setforth in the given figures, as will be appreciated by one skilled in theart.

Embodiments/examples/implementations as described may also be carriedout in the form of a computer process defined by a computer program orportions thereof. Embodiments of the methods described in connectionwith FIGS. 2 to 7 may be carried out by executing at least one portionof a computer program comprising corresponding instructions. Thecomputer program may be in source code form, object code form, or insome intermediate form, and it may be stored in some sort of carrier,which may be any entity or device capable of carrying the program. Forexample, the computer program may be stored on a computer programdistribution medium readable by a computer or a processor. The computerprogram medium may be, for example but not limited to, a record medium,computer memory, read-only memory, electrical carrier signal,telecommunications signal, and software distribution package, forexample. The computer program medium may be a non-transitory medium, forexample. Coding of software for carrying out the embodiments as shownand described is well within the scope of a person of ordinary skill inthe art. In an embodiment, a computer-readable medium comprises saidcomputer program.

Even though the invention has been described above with reference toexamples/embodiments according to the accompanying drawings, it is clearthat the invention is not restricted thereto but can be modified inseveral ways within the scope of the appended claims. Therefore, allwords and expressions should be interpreted broadly and they areintended to illustrate, not to restrict, the embodiment. It will beobvious to a person skilled in the art that, as technology advances, theinventive concept can be implemented in various ways. Further, it isclear to a person skilled in the art that the described embodiments may,but are not required to, be combined with other embodiments in variousways.

The invention claimed is:
 1. An apparatus, comprising: at least oneprocessor; and at least one memory including computer program code, theat least one memory and computer program code configured to, with the atleast one processor, cause the apparatus at least to perform:determining, for a set of user devices, at least one past beam sequenceindicating one or more beams, which served the set of user devices,wherein the set of user devices comprises one or more user devices;inputting the at least one past beam sequence to a trained model whichoutputs a probability distribution among the one or more beams toindicate a probability that each beam serves the set of user devices;determining a number of beams the set of user devices is to monitor;determining a beam group for the set of user devices by selecting fromthe probability distribution of the one or more beams according to afirst criteria as many beams as is the number of beams; and configuringthe set of user devices to monitor and report beams in the beam group.2. The apparatus according to claim 1, wherein the at least one memoryand computer program code are further configured to, with the at leastone processor, cause the apparatus to input as the past beam sequence atleast beam indexes of the past beams.
 3. The apparatus according toclaim 1, wherein the first criteria is to select the beams according toa probability order.
 4. The apparatus according to claim 1, wherein theat least one memory and computer program code are further configured to,with the at least one processor, cause the apparatus to, when theprobability distribution is a matrix, perform the following to determinethe beam group: concatenating the outputs of the matrix to a firstvector; defining a second vector having beam indexes repeated so that alength of the second vector equals to the first vector; sorting thefirst vector in a descending order; shuffling the second vectorcorrespondingly to the sorting, a result being a third vector;eliminating repetitions from the third vector; and selecting from thethird vector, starting from the beginning, to the beam group as manybeam indexes as is the number of beams the set of user devices is tomonitor.
 5. The apparatus according to claim 1, wherein the at least onememory and computer program code are further configured to, with the atleast one processor, cause the apparatus to determine the number ofbeams the set of user devices is to monitor at least based on userdevice capabilities and capabilities of the apparatus.
 6. The apparatusaccording to claim 1, wherein the at least one memory and computerprogram code are further configured to, with the at least one processor,cause the apparatus to determine a length of the past beam sequencebased on user device mobility characteristics and/or based on channelcharacteristics and/or based on beam widths.
 7. The apparatus accordingto claim 1, wherein the at least one memory and computer program codeare further configured to, with the at least one processor, cause theapparatus to store received reports comprising beam measurement results.8. The apparatus according to claim 7, wherein the at least one memoryand computer program code are further configured to, with the at leastone processor, cause the apparatus to train the trained model byperforming: determining, from past beam measurement reports,set-specifically for sets of user devices, past beams that served a setof user device within a first time interval, and future beams thatserved the set of user device within a second time interval followingthe first time interval; determining from the future beams anon-repetitive set of future beams, wherein the non-repetitive setcomprises a beam only once; and using the past beams as training dataand corresponding non-repetitive sets as validation data.
 9. Theapparatus according to claim 1, wherein the model is based on aconvolutional neural network and is configured to output the probabilitydistribution in a form of a matrix.
 10. An apparatus, comprising: atleast one processor; and at least one memory including computer programcode, the at least one memory and computer program code configured to,with the at least one processor, cause the apparatus at least toperform: determining, from past beam measurement reports, setspecifically for sets of user devices, past beams that served a set ofuser device within a first time interval, and future beams that servedthe set of user device within a second time interval following the firsttime interval, wherein a set of user devices comprises one or more userdevices; determining from the future beams a non-repetitive set offuture beams, wherein the non-repetitive set comprises a beam only once;and using the past beams as training data and correspondingnon-repetitive sets as validation data to train a model which outputs aprobability distribution of the future beams to indicate a probabilitythat each beam serves the set of user devices.
 11. The apparatusaccording to claim 10, wherein the model is based on a convolutionalneural network and is configured to output the probability distributionin a form of a matrix.
 12. A method, comprising: determining, for a setof user devices, at least one past beam sequence indicating one or morebeams, which served the set of user devices, wherein the set of userdevices comprises one or more user devices; inputting the at least onepast beam sequence to a trained model which outputs a probabilitydistribution among the one or more beams to indicate a probability thateach beam serves the set of user devices; determining a number of beamsthe set of user devices is to monitor; determining a beam group for theset of user devices by selecting from the probability distribution ofthe one or more beams according to a first criteria as many beams as isthe number of beams; and configuring the set of user devices to monitorand report beams in the beam group.
 13. The method according to claim12, further comprising: inputting as the past beam sequence at leastbeam indexes of the past beams.
 14. The method according to claim 12,wherein the first criteria is to select the beams according to aprobability order.
 15. The method according to claim 12, the methodfurther comprising: when the probability distribution is a matrix,perform the following to determine the beam group: concatenating outputsof the matrix to a first vector; defining a second vector having beamindexes repeated so that the length of the second vector equals to thefirst vector; sorting the first vector in a descending order; shufflingthe second vector correspondingly to the sorting, a result being a thirdvector; eliminating repetitions from the third vector; and selectingfrom the third vector, starting from the beginning, to the beam group asmany beam indexes as is the number of beams the set of user devices isto monitor.
 16. The method according to claim 12, further comprising:determining the number of beams the set of user devices is to monitor atleast based on user device capabilities and capabilities of theapparatus.
 17. The method according to claim 12, further comprising:determining a length of the past beam sequence based on user devicemobility characteristics and/or based on channel characteristics and/orbased on beam widths.
 18. The method according to claim 12, furthercomprising: storing received reports comprising beam measurementresults.
 19. The method according to claim 18, further comprising:training the trained model by performing: determining, from past beammeasurement reports, set specifically for sets of user devices, pastbeams that served a set of user device within a first time interval, andfuture beams that served the set of user device within a second timeinterval following the first time interval; determining from the futurebeams a non-repetitive set of future beams, wherein the non-repetitiveset comprises a beam only once; and using the past beams as trainingdata and corresponding non-repetitive sets as validation data.
 20. Themethod according to claim 12, wherein the model is based on aconvolutional neural network and is configured to output the probabilitydistribution in a form of a matrix.